# Federated Knowledge Graph Completion via Latent Embedding Sharing and Tensor Factorization

arXiv: https://arxiv.org/abs/2311.10341 · HTML: https://mlsanity.com/paper/2311.10341/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 25 references that are in the map.
- First posted on arXiv 17 Nov 2023.

## Builds on (most cited first, 17 of 17)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [Complex Embeddings for Simple Link Prediction](https://mlsanity.com/paper/1606.06357/) (2016)
- [RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space](https://mlsanity.com/paper/1902.10197/) (2019)
- [Convolutional 2D Knowledge Graph Embeddings](https://mlsanity.com/paper/1707.01476/) (2017)
- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [TuckER: Tensor Factorization for Knowledge Graph Completion](https://mlsanity.com/paper/1901.09590/) (2019)
- [A Survey on Knowledge Graph-Based Recommender Systems](https://mlsanity.com/paper/2003.00911/) (2020)
- [Knowledge Graph Embedding for Link Prediction: A Comparative Analysis](https://mlsanity.com/paper/2002.00819/) (2020)
- [FedLab: A Flexible Federated Learning Framework](https://mlsanity.com/paper/2107.11621/) (2021)
- [FedE: Embedding Knowledge Graphs in Federated Setting](https://mlsanity.com/paper/2010.12882/) (2020)
- [Differentially Private Federated Knowledge Graphs Embedding](https://mlsanity.com/paper/2105.07615/) (2021)
- [Federated Tensor Factorization for Computational Phenotyping](https://mlsanity.com/paper/1704.03141/) (2017)
- [Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation](https://mlsanity.com/paper/2203.09553/) (2022)
- [Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applications](https://mlsanity.com/paper/2207.11812/) (2022)
- [Federated Non-negative Matrix Factorization for Short Texts Topic Modeling with Mutual Information](https://mlsanity.com/paper/2205.13300/) (2022)
- [Mitigating Cross-client GANs-based Attack in Federated Learning](https://mlsanity.com/paper/2307.13314/) (2023)

## Built on by (most cited first, 5 of 5)

- [Comprehending Knowledge Graphs with Large Language Models for Recommender Systems](https://mlsanity.com/paper/2410.12229/) (2024)
- [Learning Federated Neural Graph Databases for Answering Complex Queries from Distributed Knowledge Graphs](https://mlsanity.com/paper/2402.14609/) (2024)
- [Learning Granularity Representation for Temporal Knowledge Graph Completion](https://mlsanity.com/paper/2408.15293/) (2024)
- [Renormalization Group Guided Tensor Network Structure Search](https://mlsanity.com/paper/2512.24663/) (2025)
- [T-GINEE: A Tensor-Based Multilayer Graph Representation Learning](https://mlsanity.com/paper/2605.28300/) (2026)

## Abstract (arXiv)

Knowledge graphs (KGs), which consist of triples, are inherently incomplete and always require completion procedure to predict missing triples. In real-world scenarios, KGs are distributed across clients, complicating completion tasks due to privacy restrictions. Many frameworks have been proposed to address the issue of federated knowledge graph completion. However, the existing frameworks, including FedE, FedR, and FEKG, have certain limitations. = FedE poses a risk of information leakage, FedR's optimization efficacy diminishes when there is minimal overlap among relations, and FKGE suffers from computational costs and mode collapse issues. To address these issues, we propose a novel method, i.e., Federated Latent Embedding Sharing Tensor factorization (FLEST), which is a novel approach using federated tensor factorization for KG completion. FLEST decompose the embedding matrix and enables sharing of latent dictionary embeddings to lower privacy risks. Empirical results demonstrate FLEST's effectiveness and efficiency, offering a balanced solution between performance and privacy. FLEST expands the application of federated tensor factorization in KG completion tasks.